Subgradient Algorithm, Stochastic Subgradient Algorithm, Incremental Subgradient Algorithm, and Set Location Problems
Year: 2013
Type: preprint
Abstract: In recent years, important progress has been made in applying methods and techniques of convex optimization to many fields of applications such as location science, engineering, computational statistics, and computer science. In this paper, we present some simple algorithms for solving a number of new models of facility location involving sets which generalize the classical Fermat-Torricelli problem and the smallest enclosing circle problem. The general nondifferentiability of the models prevents us from applying gradient-type algorithms, so our approach is to use subgradient-type algorithms instead. The algorithms presented in this paper are easy to implement and applicable for a broad range of problems that are also suitable for teaching and learning convex optimization.
Source: arXiv (Cornell University)
Authors: Nam, Nguyen Mau, An, Nguyen Thai, Le, Han
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1503069380